Economic burden of systemic sclerosis: systematic review
Bibliographic record
Abstract
Background. Systemic sclerosis (SSc) is a severe orphan disease, one of the systemic pathologies of connective tissue. This disease has a significant negative impact on the patient’s quality of life and has a high mortality rate. Treatment of its various complications imposes a great financial burden on the healthcare system. The difficulties of daily functioning and social adaptation and the overall burden of SSc for patients, as well as their caregivers, also contribute to the economic component of the disease. Aim. To assess the social and economic burden of SSc. Materials and methods. A systematic review was conducted according to the PRISMA guidelines using predefined PICO(S) criteria. The search was carried out in December 2019 using the MeSH terms in the Embase, MEDLINE / PubMed, Cochrane library databases. The publication date range was 10 years. To identify Russian-language studies, an additional search was conducted in eLIBRARY.ru and the Internet network. The evidence levels of the included studies were determined. Results. A total of 934 studies were identified from all databases; 53 publications were selected for eligibility; 9 of which were included in the final review. There were no studies identified to assess the burden of SSc in Russia, so the evaluations were based on foreign studies. The estimates of the annual direct costs per patient with SSc for the past decade were almost similar in different countries: 5 038 Canadian dollars in Canada, 11 607 Australian dollars in Australia, 17 365-22 016 US dollars in the USA, and 1 413-17 300 Euros in Europe (an average of about 8 000 euros). The cost structure was dominated by direct medical costs for hospitalization and drug therapy and indirect costs were mostly associated with the loss of productivity and early retirement. The costs associated with the diffuse cutaneous form of SSc were statistically higher if compared to the costs for the limited form of the disease. Among the clinical manifestations of the disease, lung lesions and gastrointestinal problems made the largest contribution to the economic burden. Conclusion. SSc is associated with significant healthcare resource use compared to the general population. The economic burden of SS has grown significantly in recent years, and this trend is global. At the same time, it is difficult to evaluate the disease costs in Russia due to a lack of information on the patient population.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".